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| Parravicini, Valeriano; Casey, Jordan M.; Schiettekatte, Nina M. D.; Brandl, Simon J.; Pozas-schacre, Chloé; Carlot, Jérémy; Edgar, Graham J.; Graham, Nicholas A. J.; Harmelin-vivien, Mireille; Kulbicki, Michel; Strona, Giovanni; Stuart-smith, Rick D.. |
| Understanding species’ roles in food webs requires an accurate assessment of their trophic niche. However, it is challenging to delineate potential trophic interactions across an ecosystem, and a paucity of empirical information often leads to inconsistent definitions of trophic guilds based on expert opinion, especially when applied to hyperdiverse ecosystems. Using coral reef fishes as a model group, we show that experts disagree on the assignment of broad trophic guilds for more than 20% of species, which hampers comparability across studies. Here, we propose a quantitative, unbiased, and reproducible approach to define trophic guilds and apply recent advances in machine learning to predict probabilities of pairwise trophic interactions with high... |
| Tipo: Text |
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| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00688/79980/82934.pdf |
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